CN114153954A

CN114153954A - Test case recommendation method and device, electronic equipment and storage medium

Info

Publication number: CN114153954A
Application number: CN202111250702.1A
Authority: CN
Inventors: 刘琮玮; 张静军; 姜琳
Original assignee: Beijing Sogou Technology Development Co Ltd
Current assignee: Beijing Sogou Technology Development Co Ltd
Priority date: 2021-10-26
Filing date: 2021-10-26
Publication date: 2022-03-08

Abstract

The invention discloses a test case recommendation method, a test case recommendation device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring query keywords for querying test cases; acquiring a target node keyword matched with the query keyword from a node keyword set corresponding to the test case tree set; and determining N target test case subtrees from the test case tree set based on the target nodes corresponding to the target node keywords as recommended test cases, wherein N is a positive integer. According to the scheme, the test case subtrees related to the query keywords can be effectively recommended to the user for the user to refer to or modify, the user does not need to write the test cases from the beginning, the writing cost is greatly reduced, and the efficiency is improved.

Description

Test case recommendation method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of computers, in particular to a test case recommendation method and device, electronic equipment and a storage medium.

Background

In order to test whether the software function can normally operate, the software function is usually required to be tested by using a test case. In the prior art, when software function testing is performed, testers need to write corresponding test cases by themselves, and due to the fact that the number of objects to be tested is large, the testers need to write a large number of test cases, labor cost is high, and efficiency is low.

Disclosure of Invention

The embodiment of the invention provides a method and a device for recommending test cases, electronic equipment and a storage medium, and solves the technical problems of high labor cost and low efficiency caused by test case compiling in the prior art.

In a first aspect, an embodiment of the present invention provides a test case recommendation method, including:

acquiring query keywords for querying test cases;

acquiring a target node keyword matched with the query keyword from a node keyword set corresponding to the test case tree set;

and determining N target test case subtrees from the test case tree set based on the target nodes corresponding to the target node keywords as recommended test cases, wherein N is a positive integer.

Optionally, for each test case tree in the test case tree set, constructing by:

constructing a root node of a test case tree based on the name of the test set;

constructing non-leaf nodes of a test case tree based on test contents contained in the test set, wherein parent nodes in the non-leaf nodes and corresponding child nodes meet the inclusion relationship;

and constructing leaf nodes of the test case tree based on the test case names contained in the test set, and adding corresponding test case content information under each leaf node.

Optionally, the determining N target test case subtrees from the test case tree set based on the target node corresponding to the target node keyword includes:

determining M target nodes corresponding to the target node keywords based on a preset corresponding relation between the node keywords and the nodes, wherein M is a positive integer greater than or equal to N;

and determining the N target test case subtrees based on the M target nodes.

The preset corresponding relation between the node keywords and the nodes is constructed by the following steps:

acquiring node text information of each node contained in the test case tree set;

for each node, performing word segmentation processing on the node text information of the node, and performing importance degree score calculation on each node word obtained by word segmentation processing; and determining the node keywords corresponding to the node based on the importance degree scores of the participles of each node of the node.

Optionally, the determining the N target test case subtrees based on the M target nodes includes:

determining M target test case subtrees corresponding to the M target nodes based on the node types of the M target nodes, wherein for each target node, when the node type of the target node is a non-leaf node, the test case subtree corresponding to the target node is used as the target test case subtree; when the node type of the target node is a leaf node, taking a test case sub-tree corresponding to a father node of the target node as the target test case sub-tree;

and performing deduplication processing on the M target test case subtrees to obtain the N target test case subtrees.

Optionally, after determining N target test case subtrees from the test case tree set, the method further includes:

classifying the N target test case subtrees based on the preset type of each test case tree in the test case tree set;

and sequencing the target test case subtrees under each type based on the correlation degree of the test case subtrees and the query keywords.

determining an associated test case subtree corresponding to each target test case subtree based on the association relationship among the test case subtrees in the test case tree set;

and recommending the associated test case subtrees corresponding to each target test case subtree.

Optionally, the association relationship between the test case subtrees in the test case tree set is determined by:

acquiring a subtree keyword set corresponding to each test case subtree, wherein the subtree keyword set comprises node keywords of all nodes in the corresponding test case subtree;

and taking the test case subtrees in the test case tree set as the current test case subtrees in sequence, and executing the following correlation degree calculation steps: respectively carrying out similarity calculation on the subtree keyword set corresponding to the current test case tree and the subtree keyword sets of other test case subtrees; and determining an associated test case subtree corresponding to the current test case subtree based on the similarity calculation result.

In a second aspect, an embodiment of the present invention provides a test case recommendation apparatus, including:

the acquisition module is used for acquiring query keywords for querying the test cases;

the matching module is used for acquiring target node keywords matched with the query keywords from the node keyword set corresponding to the test case tree set;

and the processing module is used for determining N target test case subtrees from the test case tree set based on the target nodes corresponding to the target node keywords as recommended test cases, wherein N is a positive integer.

Optionally, for each test case tree in the test case tree set, constructing by:

constructing a root node of a test case tree based on the name of the test set;

Optionally, the processing module is configured to:

determining the N target test case subtrees based on the M target nodes;

Optionally, the processing module is configured to:

Optionally, the apparatus further comprises:

the classification module is used for classifying the N target test case subtrees based on the preset type of each test case tree in the test case tree set;

and the sequencing module is used for sequencing the target test case subtrees under each type based on the correlation degree of the test case subtrees and the query keywords.

Optionally, the apparatus further comprises:

the association module is used for determining an associated test case subtree corresponding to each target test case subtree based on the association relationship among the test case subtrees in the test case tree set;

and the recommending module is used for recommending the associated test case subtrees corresponding to each target test case subtree.

In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and one or more programs, where the one or more programs are stored in the memory and configured to be executed by one or more processors to execute operation instructions included in the one or more programs for performing the test case recommendation method according to the first aspect.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps corresponding to the test case recommendation method provided in the first aspect.

One or more technical solutions provided by the embodiments of the present invention at least achieve the following technical effects or advantages:

the method provided by the embodiment of the invention comprises the steps of obtaining query keywords for querying a test case, and obtaining target node keywords matched with the query keywords from a node keyword set corresponding to a test case tree set; and determining N target test case subtrees from the test case tree set based on the target nodes corresponding to the target node keywords as recommended test cases. In the above scheme, a target node matched with the query keyword is determined in the test case tree through the test case set with the tree structure, and the node keyword of the target node is matched with the query keyword, so that the target test case subtree determined based on the target node is the test case subtree associated with the query keyword. Therefore, the method and the device can effectively recommend the test case subtree related to the query keyword to the user for the user to refer to or modify, the user does not need to write the test case from the beginning, the writing cost is greatly reduced, and the efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a flowchart of a test case recommendation method according to an embodiment of the present invention;

FIG. 2 is a diagram illustrating a test case tree according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a test case recommendation apparatus according to an embodiment of the present invention;

fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of a server according to an embodiment of the present invention.

Detailed Description

The invention provides a test case recommendation method, a test case recommendation device, electronic equipment and a storage medium, which are used for solving the technical problems of high labor cost and low efficiency caused by test case compiling in the prior art, and have the following general ideas:

acquiring query keywords for querying test cases; acquiring target node keywords matched with the query keywords from the node keyword set corresponding to the test case tree set; and determining N target test case subtrees from the test case tree set based on the target nodes corresponding to the target node keywords as recommended test cases, wherein N is a positive integer.

In the above scheme, a target node matched with the query keyword is determined in the test case tree through the test case set with the tree structure, and the node keyword of the target node is matched with the query keyword, so that the target test case subtree determined based on the target node is the test case subtree associated with the query keyword. Therefore, the method and the device can effectively recommend the test case subtree related to the query keyword to the user for the user to refer to or modify, the user does not need to write the test case from the beginning, the writing cost is greatly reduced, and the efficiency is improved.

Referring to fig. 1, a flowchart of a test case recommendation method according to an embodiment of the present invention is shown, where the method includes the following steps:

step S101: acquiring query keywords for querying test cases;

step S102: acquiring a target node keyword matched with the query keyword from a node keyword set corresponding to the test case tree set;

step S103: and determining N target test case subtrees from the test case tree set based on the target nodes corresponding to the target node keywords as recommended test cases.

The method provided by the embodiment of the invention can be applied to terminal equipment, such as a smart phone and a tablet computer, can also be applied to a server which is established with data interaction with the terminal equipment, and can also be applied to a system which consists of the terminal equipment and the server, and the method is not limited here.

In the embodiment of the invention, an application program client for inquiring the test case can be operated in the terminal equipment of the user, the client can display an interactive page to the user, the interactive page can receive the test case inquiry content input by the user, and the recommended test case can be displayed to the user.

In step S101, an input operation of a user, such as a manual input or a voice input, may be received through an application client running on a user terminal for querying a test case. After the input operation of the user is detected, an input text corresponding to the input operation of the user is obtained, word segmentation processing is carried out on the input text to obtain word segmentation results, and the word segmentation results are used as query keywords. Wherein the number of query keywords may be one or more.

In the embodiment of the invention, in order to recommend the test case more comprehensively, after the word segmentation is carried out on the text input by the user, the word segmentation result is used as the query key word, and besides, the associated words with similar semanteme to the word segmentation result can be determined and also used as the query key word to expand the query key word.

After the query keyword is obtained, step S102 is executed to determine a target node keyword matched with the query keyword from the node keyword set corresponding to the test case tree set.

In the embodiment of the invention, the test case tree set consists of one or more constructed test case trees. The method comprises the following steps of for each test case tree: constructing a root node of a test case tree based on the name of the test set; constructing non-leaf nodes of a test case tree based on test contents contained in the test set, wherein parent nodes in the non-leaf nodes and corresponding child nodes meet the inclusion relationship; and constructing leaf nodes of the test case tree based on the test case names contained in the test set, and adding corresponding test case content information under each leaf node.

It should be noted that each test case tree corresponds to all test cases in one test set, and the test case tree includes a root node, a non-leaf node, and a leaf node.

Specifically, the test set name is filled in the root node of the test case tree, and of course, other contents for characterizing the test case set may be filled in the root node, for example, the test item name is filled in the root node.

And filling test contents in non-leaf nodes of the tree, wherein the test contents can include but are not limited to test objects (such as input boxes, link entries and the like), test categories (such as function tests, security tests, performance tests and the like), specific functions (such as app login), specific situations (such as login by using account numbers which are not used for a long time) and the like. In the embodiment of the invention, strict inclusion relation is followed for the parent node and the child nodes in the non-leaf nodes. For example, the node a is a parent node of the node B, the node B is a parent node of the node C, and the test contents corresponding to the node a, the node B and the node C are sequentially a login-input box-a password input box.

And filling the test case name in a leaf node of the tree, and adding corresponding test case content information to the leaf node to serve as annotation information of the leaf node. The test case content information includes, but is not limited to, preconditions, test steps, expected results, test categories, and the like of the test case.

For better understanding of the test case tree in the embodiment of the present specification, please refer to fig. 2, which is a schematic diagram of a test case tree provided in the embodiment of the present specification. As shown in fig. 2, the root node of the tree is "login" and corresponds to the test set name; the root node comprises two child nodes, and the corresponding test contents are respectively 'log-out page' and 'mobile phone number log-in'.

The child node "log-out page" may include two leaf nodes, and the corresponding test case names are "check some modules do not show" and "do not show the page module from log-in to log-out". The content information of the test case corresponding to the leaf node "checking the non-display condition of some modules" may include: { preconditions (none); a testing step (logging out of a login page, checking that some modules are not displayed); expected results (meeting demand expectations) }. The test case content information corresponding to the "no login to exit page, no display condition of the page module" of the leaf node may include: { preconditions (none); testing step (from not logged in to logged out page); the expected result (the situation not shown by the page module is expected) }.

Further, the child node "mobile phone number login" may include two leaf nodes, and the corresponding test case names are "correct input" and "incorrect input", respectively. The test case content information corresponding to the "correct input" of the leaf node may include: { preconditions (none); testing (inputting mobile phone number and verification code correctly); expected outcome (login success); test class (functional test). The test case content information corresponding to the leaf node "error input" may include: { preconditions (none); testing step (inputting mobile phone number and verification code by mistake); expected results (login failure, pop-up error prompt); test class (functional test).

By the method, the corresponding test case tree can be constructed for each test set or test item, and the test cases can be systematically and comprehensively described, so that subtrees in the test case tree are recommended to the user as recommended test cases, and an effective and reasonable test case frame can be provided for the user to be directly used or rewritten by the user.

It should be noted that, in order to facilitate recommending different types of test cases to a user, in the embodiment of the present invention, a test case tree may also be classified, where the types of the test case tree include, but are not limited to, the following: expert use cases, project use cases, other use cases. The expert case can comprise a general test case tree which is compiled for a test expert, audited and used; the project case can comprise a test case tree compiled for a certain project by a tester; other use cases may include all test case trees other than the types described above.

Further, for each test case tree in the test case tree set, each node of the test case tree corresponds to node text information (such as a test set name, test content, a test case name, test case content information, and the like).

Specifically, the node text information of each node contained in the test case tree set is segmented, the importance degree score of each segmented word is evaluated, segmented words with the scores larger than the preset score are used as node keywords of the node, or the segmented words are sequenced according to the order of the importance degree scores from large to small, K segmented words in the top K positions are used as node keywords of the node, and K is a positive integer.

In particular implementations, evaluating the importance scores of each participle may be accomplished in a number of ways. In one embodiment, the TF-IDF score of each participle may be calculated as the importance score of the participle by TF-IDF (term frequency-inverse document frequency). In another embodiment, the TextRank score of each participle may be calculated by TextRank (text arrangement) as the importance score of the participle.

In the embodiment of the present specification, when evaluating the importance degree score of each participle, the method may be implemented as follows: and aiming at each test field, constructing a keyword set in the test field, wherein the keyword set can be manually selected, and can also be automatically generated by performing keyword analysis on a test case in the test field. For each node in the test case tree set, determining a test field corresponding to the test case tree where the node is located, further performing word segmentation on node text information of each node to obtain a node word segmentation, judging whether the node word segmentation corresponding to the node hits a keyword set in the corresponding test field, scoring the node word segmentation based on a hit result, and marking a score as a first score of the node word segmentation. Meanwhile, TF-IDF scores of the node participles are calculated as second scores, and TextRank scores of the node participles are calculated as third scores. And performing weighted calculation on the first score, the second score and the third score to obtain a score of the evaluation node word segmentation importance degree. The weights of the first score, the second score and the third score may be set according to actual needs, and are not limited herein.

By the method, the importance degree score of each node word segmentation of the node is obtained, word segmentation screening is carried out based on the importance degree score of each node word segmentation, and the node keyword corresponding to each node is obtained. Furthermore, the node keywords of all nodes included in the test case tree set form a node keyword set corresponding to the test case tree set.

Further, the obtained query keyword is matched with the node keyword set, and in one embodiment, the query keyword may be matched with each node keyword in the node keyword set to obtain a target node keyword matched with the query keyword. In another embodiment, in order to save computing resources, the node keywords in the node keyword set may be clustered based on similarity, so that after the query keyword is obtained, at least one category related to the query keyword is determined from the categories included in the node keyword set, and the query keyword is respectively matched with the node keywords belonging to the at least one category in the node keyword set to obtain a target node keyword matched with the query keyword, without matching the query keyword with all the node keywords in the node keyword set.

In the embodiment of the present specification, whether the query keyword and the node keyword are matched may be determined by whether the node keyword includes all of the query keywords or a part of the query keywords. For example, when the node keyword contains all or part of the query keyword, it is determined that the query keyword and the node keyword are successfully matched.

After determining the target node keywords matching the query keywords, step S103 is performed, i.e., N target test case subtrees are determined from the test case tree set based on the target nodes corresponding to the target node keywords.

In the embodiment of the present specification, step S103 may be implemented in various ways. In one embodiment, for a target node, the path from the target node to the root node may be used as a target testcase subtree. In one embodiment, for a target node, a test case subtree with the target node as a root node may be used as a target test case subtree. In another embodiment, when there are a plurality of target nodes, the position relationship between each target node may be determined, and when there are a plurality of target nodes located in the same testcase subtree, the testcase subtree is taken as a target testcase subtree. Of course, the target test case subtree may also be determined in other ways, which is not limited herein.

In this embodiment, step S103 may be implemented by: determining M target nodes corresponding to the target node keywords based on a preset corresponding relation between the node keywords and the nodes, wherein M is a positive integer larger than N; and determining the N target test case subtrees based on the M target nodes.

Specifically, the preset correspondence between the node keyword and the node may be preset, and the correspondence between the node keyword and the node may be constructed by: acquiring node text information of each node contained in the test case tree set; for each node, performing word segmentation processing on the node text information of the node, and performing importance degree score calculation on each node word obtained by word segmentation processing; and determining the node keywords corresponding to the node based on the importance degree scores of the participles of each node of the node.

Specifically, the word segmentation, the calculation of the importance degree score, and the determination of the node keyword of the node text information may refer to the above description, and will not be described herein again. Further, when the node keywords are generated, the node keywords may be associated with corresponding nodes, and inverted indexes may be performed according to the node keywords. Therefore, M target nodes corresponding to the target node keywords can be determined through the preset corresponding relation between the node keywords and the nodes.

Furthermore, determining the N target test case subtrees based on the M target nodes may be implemented in various ways, and two of them are described below as examples.

The first method comprises the following steps: and determining M target test case subtrees corresponding to the M target nodes based on the node types of the M target nodes. When the node type of the target node is a non-leaf node, regarding each target node, taking a test case sub-tree corresponding to the target node as the target test case sub-tree; when the node type of the target node is a leaf node, taking a test case sub-tree corresponding to a father node of the target node as the target test case sub-tree; and performing deduplication processing on the M target test case subtrees to obtain the N target test case subtrees.

Specifically, the types of the target nodes may be a non-leaf node and a leaf node, and when the type of the target node is the non-leaf node, the target node is used as a root node, and a sub-tree formed by nodes below the target node is used as a target test case sub-tree. And when the type of the target node is a leaf node, taking a father node of the leaf node as a root node, and taking a subtree formed by the nodes below the father node as a target test case subtree.

It should be noted that, by the above method, when the number of target nodes is M, M target test case subtrees can be determined, and duplication may occur in the M target test case subtrees. For example, when the leaf node and the parent node of the leaf node are both target nodes, the target test case subtrees corresponding to the two target nodes are the same and are both test case subtrees corresponding to the parent node, at this time, the M target test case subtrees can be subjected to deduplication processing, repeated target test case subtrees are screened, and finally, N target test case subtrees are obtained.

And the second method comprises the following steps: screening out a first type of target nodes located on the same path and a second type of target nodes not located on the same path with other nodes from the M target nodes based on the position of each target node in the corresponding test case tree; aiming at the first class target node, determining a target test case sub-tree corresponding to the first class target node based on a target path corresponding to the first class target node; and aiming at the second type target node, determining a target test case sub-tree corresponding to the second type target node based on the node type of the second type target node.

Specifically, in order to avoid identifying duplicate target test case subtrees in the first method, in this embodiment, M target nodes may be screened based on their positions in the tree structure, the target nodes located on the same path may be used as first type target nodes, and the nodes other than the first type target nodes may be used as second type target nodes, where each of the second type target nodes is not located on the same path as any other node.

It should be noted that, for a target node, a path from the target node to the root node is determined, and if there are other target nodes on the path, the two target nodes may be considered to be located on the same path. For example, if the target node 1 is a parent node of the target node 2, and the target node 2 is a parent node of the target node 3, the target nodes 1, 2, and 3 are first type target nodes located on the same path. Further, the target path may be a path formed in the order from the lower level to the higher level of the target node, and following the above example, the target path corresponding to the target nodes 1, 2, and 3 is a path from the target node 1 to the target node 3. Further, the subtree where the target path is located may be used as the target test case subtree, and the above example is still used, so that the target test case subtree may use the subtree corresponding to the node 1 as the target test case subtree.

For the second type of target node, the corresponding target test case sub-tree may be determined according to the node type of the target node, which may specifically refer to the first manner, and this is not described herein again. Because the second type target node is not located in the same path as any other node, the target test case subtree generated based on the second type target node does not have the situation of duplication.

In this embodiment of the present specification, after determining the N target test case subtrees, the following steps may also be performed: classifying the N target test case subtrees based on the preset type of each test case tree in the test case tree set; and sequencing the target test case subtrees under each type based on the correlation degree of the test case subtrees and the query keywords.

Specifically, the constructed test case trees are all corresponding to respective preset types, and the preset types may include the expert cases, the project cases, and other cases. After N target test case subtrees are obtained, a preset type of a test case tree where each target test case subtree is located is determined as the type of the target test case subtree. For example, if the preset type of the test case tree in which a target test case subtree is located is an expert case, the target test case subtree is divided into the expert cases.

It should be noted that, when a test case subtree is recommended, the test case subtree can be recommended according to a preset type priority, for example, the priority order among the expert cases, the project cases, and other cases is as follows: expert use case > project use case > other use cases.

Furthermore, in each type, the target test case subtrees in the type are sorted based on the correlation degree between the target test case subtrees in the type and the query keywords, and the sorting mode can be set according to needs, which is not limited here. Two sorting methods will be described below.

First, for a target test case subtree under each type, the target test case subtree can be graded according to the relationship between the target test case subtree and a query keyword. Specifically, the target test case subtree can be classified into the following three levels: completely consistent, fully contained, partially contained. Wherein, the complete consistency means that the node keywords contained in the target test case subtree are completely consistent with the query keywords; all the contents are included, namely that the target test case subtree completely contains the query key words, but also contains other key words; partial inclusion means that only a part of the query keywords are contained in the target test case subtree.

When the test case tree is sequenced, the priority order of the three levels is as follows: fully consistent > fully inclusive > partially inclusive. Further, the target test case subtrees at each level are sorted, and in the embodiment of the present specification, for each level, sorting is performed based on the matching degree between the target test case subtrees at the level and the query keyword and the quality scores of the target test case subtrees.

Specifically, for each class, a respective matching degree calculation method may be associated.

For "perfect agreement," the matching degree between each target test case subtree and the query keyword at that level is 1.

For "all contained", for each target test case subtree at the level, the matching degree between the target test case subtree and the query keyword may be: the number of the query keywords/the number of all the node keywords contained in the target test case subtree.

For "partial inclusion", for each target test case subtree at the level, the calculation manner of the matching degree between the target test case subtree and the query keyword may be: the number of query keywords contained in the target test case subtree x weight + (number of query keywords contained in the target test case subtree/number of all node keywords contained in the target test case subtree). The weight may be set according to actual needs, and for example, the weight may be selected from 5, 8, 10, and the like.

By the method, the matching degree between each target test case subtree and the query keyword under each grade can be obtained.

Further, in the embodiment of the present specification, the quality score of the target test case subtree at each level may also be obtained. And for each target test case subtree, the quality score can be determined through the use times of the target test case subtree, the size of the target test case subtree and the integrity of the target test case subtree. For example, the number of times of use, the size and the integrity of the subtree of the target test case are weighted and averaged to obtain the quality score. Of course, the quality scores of the target test case subtrees can also be determined by other characteristics, which are not limited herein.

After the matching degree and the quality score of each target test case subtree are obtained, the correlation degree between each target test case subtree and the query keyword can be determined through weighting processing or other modes. And sequencing the target test case subtrees under each level based on the correlation degree of each target test case subtree.

In summary, in the first sorting mode, sorting is performed according to the types of the target test case subtrees, then sorting is performed according to the levels of the target test case subtrees in each type, and further sorting is performed according to the correlation degree between each target test case subtree and the query keyword in each level.

And the second method comprises the following steps: and predicting the probability of each target test case subtree clicked by the user based on the trained click rate prediction model aiming at the target test case subtrees of each type to obtain the predicted click rate of each target test case subtree. Furthermore, the target test case subtrees of each type are sorted according to the predicted click rate.

The click rate prediction model may be selected according to actual needs, for example, the click rate prediction model may be a convolutional neural network model, a linear and depth (Wide and Deep) model, or the like. The input of the click-through rate prediction model may be set according to the actual situation, for example, the input of the model includes, but is not limited to, node text information of the test case subtree, the path where the node is located, the number of times of use, the creation time, the last modification time, and the like, and the output of the model is the predicted click-through rate of the test case subtree.

In the embodiment of the present specification, in order to recommend a more comprehensive test case to a user and help the user to diverge thinking, the method may further include the following steps: determining an associated test case subtree corresponding to each target test case subtree based on the association relationship among the test case subtrees in the test case tree set; and recommending the associated test case subtrees corresponding to each target test case subtree.

Specifically, the association relationship between the test case subtrees in the test case tree set may be pre-constructed, and the construction method may be implemented as follows: acquiring a subtree keyword set corresponding to each test case subtree, wherein the subtree keyword set comprises node keywords of all nodes in the corresponding test case subtree; and taking all test case subtrees in the test case set as current test case subtrees in sequence, and executing the following correlation degree calculation steps: and respectively carrying out similarity calculation on the subtree keyword set corresponding to the current test case tree and the subtree keyword sets of other test case subtrees, and determining an associated test case subtree corresponding to the current test case subtree based on a similarity calculation result.

In the specific implementation process, each test case tree in the test case tree set comprises one or more test case subtrees, and for each test case subtree, the node keywords of all nodes contained in the test case subtree are used as a corresponding subtree keyword set. And carrying out similarity calculation on every two subtree keyword sets between the subtrees of the test cases to obtain a similarity calculation result between any two subtrees of the test cases.

In this embodiment of the present specification, if a similarity calculation result between two test case subtrees is greater than a threshold, the two test case subtrees may be considered as being associated with each other. Or, for a test case subtree, sorting the similarity calculation results between the test case tree and all other test case subtrees from large to small, and selecting the test case subtree with the largest target number of similarity calculation results as the associated test case subtree of the test case subtree.

Since N target test case subtrees are already determined in step S103, the associated test case subtrees with each target test case subtree are determined by querying the association relationship among the test case subtrees, and are recommended to the user. Therefore, the test case subtrees in the test case tree set can be recommended to the user by the largest program, the user is helped to expand thinking, and the existing test scheme is fully used for reference, so that the test cases written by the user are more comprehensive and reasonable.

It should be noted that after determining the associated test case subtrees of each target test case subtree, the associated test case subtrees may be sorted. Specifically, for each target test case subtree, the associated test case subtrees can be sorted based on the same number of node keywords, the same node keyword proportion, the node keyword weight, the case quality of the associated test case subtree, and the like between the target test case subtree and each associated test case subtree.

In order to better understand the method provided by the embodiments of the present specification, the following describes an overall recommendation flow of test cases. After receiving a query keyword input by a user, recalling N target test case subtrees from the test case tree set based on the query keyword, classifying the N target test case subtrees, such as dividing the target test case subtrees into expert cases, project cases and other cases, and sequencing the target test case subtrees under each type. And meanwhile, recalling P associated test case subtrees corresponding to each target test case subtree, and sequencing the P associated test case subtrees, wherein P is a positive integer.

Further, when the selection operation of the user for the target test case subtree or the associated test case subtree is detected, the corresponding subtree can be displayed for the user to directly use or modify.

In summary, the method in the implementation of this specification can screen out N target test case subtrees related to query keywords from a combination of pre-compiled test case trees based on the query keywords, and meanwhile, in order to help the user to diverge thinking, the associated test case subtrees corresponding to the target test case subtrees are also recommended to the user for the user to refer to, directly use or modify and use. The method avoids the user from writing the test case from the beginning, greatly saves the labor cost for writing the test case, and improves the efficiency.

Based on the same inventive concept, an embodiment of the present specification further provides a test case recommendation device, as shown in fig. 3, where the device includes:

an obtaining module 301, configured to obtain a query keyword for querying a test case;

a matching module 302, configured to obtain a target node keyword matched with the query keyword from a node keyword set corresponding to the test case tree set;

and the processing module 303 is configured to determine N target test case subtrees from the test case tree set based on the target node corresponding to the target node keyword, where N is a positive integer, and the N is used as a recommended test case.

Optionally, for each test case tree in the test case tree set, constructing by:

constructing a root node of a test case tree based on the name of the test set;

Optionally, the processing module 303 is configured to:

and determining the N target test case subtrees based on the M target nodes.

Optionally, the preset corresponding relationship between the node keywords and the nodes is constructed by the following steps:

Optionally, the processing module 303 is configured to:

Optionally, the apparatus further comprises:

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Based on the same inventive concept, an embodiment of the present invention provides an electronic device 800, and fig. 4 is a block diagram illustrating the electronic device 800 according to an exemplary embodiment. For example, the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.

Referring to fig. 4, device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing elements 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operation at the device 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

Power component 806 provides power to the various components of device 800. Power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for device 800.

The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed state of the device 800, the relative positioning of the components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communications component 816 is configured to facilitate communications between device 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communications component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Fig. 5 is a schematic diagram of a server in some embodiments of the invention. The server 1900 may vary widely by configuration or performance and may include one or more Central Processing Units (CPUs) 1922 (e.g., one or more processors) and memory 1932, one or more storage media 1930 (e.g., one or more mass storage devices) storing applications 1942 or data 1944. Memory 1932 and storage medium 1930 can be, among other things, transient or persistent storage. The program stored in the storage medium 1930 may include one or more modules (not shown), each of which may include a series of instructions operating on a server. Still further, a central processor 1922 may be provided in communication with the storage medium 1930 to execute a series of instruction operations in the storage medium 1930 on the server 1900.

The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input-output interfaces 1958, one or more keyboards 1956, and/or one or more operating systems 1941, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

A non-transitory computer-readable storage medium, in which instructions are executed by a processor of an apparatus (server or terminal), so that the apparatus can perform the data processing method of the foregoing embodiments.

A non-transitory computer-readable storage medium in which instructions, when executed by a processor of an apparatus (server or terminal), enable the apparatus to perform a test case recommendation method, the method comprising:

acquiring query keywords for querying test cases;

according to the scheme in the embodiment of the invention, the target node matched with the query keyword is determined in the test case tree through the test case with the tree structure, and the node keyword of the target node is matched with the query keyword, so that the target test case subtree determined based on the target node is the test case subtree associated with the query keyword. Therefore, the method and the device can effectively recommend the test case subtree related to the query keyword to the user for the user to refer to or modify, the user does not need to write the test case from the beginning, the writing cost is greatly reduced, and the efficiency is improved.

Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is defined only by the appended claims, and is not intended to be limited by the foregoing description, and any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention are intended to be included therein.

Claims

1. A test case recommendation method is characterized by comprising the following steps:

acquiring query keywords for querying test cases;

2. The method of claim 1, wherein for each test case tree in the set of test case trees, the method is constructed by:

constructing a root node of a test case tree based on the name of the test set;

3. The method of claim 1 or 2, wherein the determining N target test case subtrees from the test case tree set based on the target node corresponding to the target node keyword comprises:

determining the N target test case subtrees based on the M target nodes;

for each node, performing word segmentation processing on the node text information of the node, and performing importance degree score calculation on each node word obtained by word segmentation processing; and determining the node keywords corresponding to the nodes based on the importance degree scores of the participles of each node of the nodes.

4. The method of claim 3, wherein said determining the N target test case subtrees based on the M target nodes comprises:

determining M target test case subtrees corresponding to the M target nodes based on the node types of the M target nodes; when the node type of the target node is a non-leaf node, regarding each target node, taking a test case sub-tree corresponding to the target node as the target test case sub-tree; when the node type of the target node is a leaf node, taking a test case sub-tree corresponding to a father node of the target node as the target test case sub-tree;

5. The method of claim 1, wherein after the N target test case subtrees are determined from the set of test case trees, the method further comprises:

6. The method of claim 1, wherein after the N target test case subtrees are determined from the set of test case trees, the method further comprises:

7. The method of claim 6, wherein associations between test case subtrees in the set of test case trees are determined by:

8. A test case recommendation device, comprising:

9. The apparatus of claim 8, wherein for each test case tree in the set of test case trees, constructed by:

constructing a root node of a test case tree based on the name of the test set;

10. The apparatus of claim 8 or 9, wherein the processing module is to:

determining the N target test case subtrees based on the M target nodes;

11. The apparatus of claim 10, wherein the processing module is to:

12. The apparatus of claim 8, wherein the apparatus further comprises:

13. The apparatus of claim 8, wherein the apparatus further comprises:

14. An electronic device, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors to perform the corresponding operational instructions of the method according to any one of claims 1-7.

15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps corresponding to the method according to any one of claims 1 to 7.